Pilot study of Qigong/Tai Chi Easy acute effects of meditative movement, breath focus and "flow" on blood pressure, mood and oxytocin in older adults.

BACKGROUND: Older adults are increasingly lonely and at risk for hypertension. Endogenous oxytocin levels are associated with lowering blood pressure (BP), suggesting value in increasing oxytocin. Regular practice of Tai Chi improves BP and mood; we explored a single session of Tai Chi Easy (TCE) with older adults and feasibility of measuring oxytocin as a key biomarker. METHOD: In a single-arm pre-post design pilot study, 21 older adults (age 55-80) with mild-moderate hypertension practiced a single session (50-min) TCE. BP, psychosocial measures, and saliva samples were collected pre/post to examine feasibility of acute measures of oxytocin and explore effect sizes of outcomes. Participants (N = 21; 19 % Latinx, 76.2 % female, mean age 66.76). RESULTS: BP systolic: 138.43-134.86; diastolic 78.48-78.00 (p > .05; Cohen's d -0.23; -0.08 respectively). Total Mood Disturbance (TMD) and Connection (CN) improved [TMD mean pre 41.891 (SD=19.60) to post 35.00 (SD=10.21), p = .01; Cohen's d - 0.67); CN mean 7.85 (SD=2.01) to post 9.05 (SD=1.00), p = .01; Cohen's d 0.70]. Baseline oxytocin was positively correlated with baseline loneliness (N = 14, r = .599); pre/post oxytocin changes were negatively correlated with baseline loneliness (N = 14, r = -.585). BP decrease was associated with characteristics of the intervention: "flow" (coef=.=0.58N = 17) and meditative/breath focus (coef=-1.78; N = 17). DISCUSSION/CONCLUSION: Medium to large effect sizes indicating change in mood and connection were found for this single session intervention. Knowing that Tai Chi improves BP when practiced over time, this TCE intervention shows promise for planning a fully powered, randomized controlled study of BP, mood and perceptions of connection in hypertensive older adults. Feasibility of assessing acute salivary oxytocin is less promising. Increase in oxytocin levels occurred for those less lonely, but declined for lonelier participants. With different responses based on baseline loneliness scores, no mean change in oxytocin levels was found. Seemingly unstable levels (possibly related to interaction with study staff) suggests the need for further testing in more controlled study designs. Finally, BP associations with meditative/breath focus and flow could be further explored in future study designs addressing mediation.

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG.

Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.